Working with academic communities to build scientific understanding

We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.

Our release process continues to evolve in response to feedback from the associated academic communities. In our view, these manuscripts are best understood as a form of raw research data. To convert this raw research data into scientific understanding, in this release we are experimenting with a new approach. We reached out to external experts and asked them to act as shepherds, organizing groups to understand the new results. They advised three different release strategies:

• For the math result, release the AI output, leaving researchers free to organize their own efforts to explain the result.

• For the computer science result, form an external group of collaborators to explain the result, while making the AI output publicly available for anyone to examine and build on.

• For the physics result, form an external group of collaborators and defer to them on the release of materials.

We do not yet know which of these three approaches will be most useful, and we welcome feedback on what works and what we can improve.

The external collaborations will follow four principles:

We will turn over materials and artifacts related to these results to the collaborations. For the computer science collaboration, the group has decided to make the natural language proof and Lean formalization public immediately, and we are publishing them directly to our GitHub repository. For the physics collaboration, the group has decided to hold off on releasing the AI-produced artifacts and instead work on writing it up themselves.

Local smoothing and the Kakeya conjecture

The Exponential Time Hypothesis is false

This work will be verified and shepherded by a group led by Russell Impagliazzo (UC San Diego), Ramamohan Paturi (UC San Diego), and Ryan Williams (MIT). All three are experts in algorithm design and complexity theory.